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Investigation of Accuracy Reduction Due to Model Over-simplification of Engineering Systems

Faculty #90
Discipline: Technology & Engineering
Subcategory: STEM Research
- California State University, Los Angeles
Co-Author(s): Florian Dugast , Pierrick Renault and Usama Tohid, California State University, Los Angeles , Los Angeles, CA



The mathematical modeling of any engineering system, whether for design, performance prediction, optimization or control, requires assessment of level of accuracy versus simplicity of the formulation. Although it is universally accepted that the more complex the formulation the more accurate the results from it, these require larger CPU times and computer resources, and are frequently limited by the capacity of computing power, precluding their use in favor of simpler models. Many times, however, engineers do not realize the potential risk that over-simplification of a problem generates in terms of accuracy of the results, as the model solution does not resemble the system behavior. The present study addresses the issue of over-simplification of the resulting mathematical model and the corresponding accuracy of its solution. The fluid flow inside a channel — containing an obstruction — is used as a demonstrative example. After constructing three sets of models of the physical system, each with a different level of detail, numerical solutions are compared to experimental data. The results show that the accuracy of the numerical approximation depends directly on the level of complexity of the mathematical model, and that over-simplification may result in up to a nine-fold degradation of the results. In addition, minor changes in the inlet boundary condition and geometry result in large changes in the flow pattern, with up to a five-fold difference in the recirculation bubble relative error. This information is fundamental for engineering professionals to consider during the modeling process for applications.

Funder Acknowledgement(s): NSF IIP-0844891 ; HRD-1547723 ; ARA-R2-0963539

Faculty Advisor: None Listed,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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